How to pass 5D tensor to nn.Linear() function

Hello all, I have a 5D tensor such as BxCxHxWxD. I want to use nn.Linear function to classify from C to C//2 channel. How should I do it?

This is my code

class 5D_Linear(nn.Module):
    def __init__(self, channel):
        super(5D_Linear, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool3d(1)
        self.linear_5d = nn.Sequential(nn.Linear(channel, channel// 2),
                                                 nn.Linear(channel // 2, channel))

    def forward(self, x):
        x = self.avg_pool(x)
        B, C, D, H, W = x.size()       
        x_4d = x.view(B, C, D, -1)
        x_4d = self.linear_5d(x_4d)
        x_5d = x_4d.view(B, C, D, H, W)

        return x_5d

linear_5d seems to be mapping channel//2 back to channel , is that your intention? what doesn’t seem to be working in this code?

sorry. this is my mistake. the 5d must convert to 1d to perform classification. it looks compute prob of each channel